Resiliency or data protection of enterprise data in a system is a complex task, achieved by deploying a combination of known and different replication-based strategies for replicating the enterprise data. Multiple sets of metadata provided by these different replication-based strategies are separately managed and stored by different replication utilities and tools. The exact combination of replication-based strategies varies based on the type of data protection, availability of resources, and performance overhead thresholds. Furthermore, replication-based strategies evolve over time and according to changes in values of data. Moreover, the use of multiple replication-based strategies leads to a requirement for multiple administrators and multiple hardware and software stacks to implement the strategies. Because of the complexity of the replication-based strategies, finding a recovery point or recoverable replica in a case of disaster recovery is difficult due to replication metadata being spread across the multiple replication-based strategies. Correlating this replication metadata across the multiple strategies is a nontrivial matter. Further, finding the required application or user data may require mounting and restoring of recovery points and checking for the existence of the data in a trial-and-error manner until the required recovery point is found, thereby significantly increasing the recovery time and impacting system availability and downtime. The usability and effectiveness of a replication strategy depends on the speed of recovery (i.e., identifying and restoring the replica that includes the desired data item) and the impact on production (i.e., the length of time windows required for backup and replication, and the impact on instantaneous production throughput). Known approaches to indexing replicated data involve brute force crawling or mining of a complete dataset to extract or build an index of keywords and storing the index in a memory or other computer data repository. The known indexing approaches are data intensive operations that impact system resource usage and/or production applications. A known approach of indexing replicas one-by-one or in-parallel has a significant impact on system resource usage because the rate at which replicas are generated is relatively high due to aggressive resiliency requirements. In many systems, replicated content may be growing about 10 times as fast as the regular data and/or the replicated content may be 10 to 20 times the size of the regular data. Known in-band indexing and out-of-band indexing approaches are also data intensive operations.